Abstract

Digital repeat photography has the potential to become an important long-term data source for phenological research given its advantages in terms of logistics, continuity, consistency and objectivity over traditional assessments of vegetation status by human observers. Red-green-blue (RGB) color channel information from digital images can be separately extracted as digital numbers, and subsequently summarized through color indices such as excess green (ExG = 2G − [R + B]) or through nonlinear transforms to chromatic coordinates or other color spaces. Previous studies have demonstrated the use of ExG and the green chromatic coordinate (gcc = G/[R + G + B]) from digital landscape image archives for tracking canopy development but several methodological questions remained unanswered. These include the effects of diurnal, seasonal and weather-related changes in scene illumination on ExG and gcc, and digital camera and image file format choice.

We show that gcc is generally more effective than ExG in suppressing the effects of changes in scene illumination. To further reduce these effects we propose a moving window approach that assigns the 90th percentile of all daytime values within a three-day window to the center day (per90), resulting in three-day ExG and gcc. Using image archives from eleven forest sites in North America, we demonstrate that per90 is able to further reduce unwanted variability in ExG and gcc due to changes in scene illumination compared to previously used mean mid-day values of ExG and gcc.

Comparison of eleven different digital cameras at Harvard Forest (autumn 2010) indicates that camera and image file format choice might be of secondary importance for phenological research: with the exception of inexpensive indoor webcams, autumn patterns of changes in gcc and ExG from images in common JPEG image file format were in good agreement, especially toward the end of senescence. Due to its greater effectiveness in suppressing changes in scene illumination, especially in combination with per90, we advocate the use of gcc for phenological research. Our results indicate that gcc from different digital cameras can be used for comparing the timing of key phenological events (e.g., complete leaf coloring) across sites. However, differences in how specific cameras “see” the forest canopy may obscure subtle phenological changes that could be detectable if a common protocol was implemented across sites.